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A Scalable and Feasible Matrix Completion Approach Using Random Projection

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

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Abstract

The low rank matrix completion problem has attracted great attention and been widely studied in collaborative filtering and recommendation systems. The rank minimization problem is NP-hard, so the problem is usually relaxed into a matrix nuclear norm minimization. However, the usage is limited in scability due to the high computational complexity of singular value decomposition (SVD). In this paper we introduce a random projection to handle this limitation. In particular, we use a randomized SVD to accelerate the classical Soft-Impute algorithm for the matrix completion problem. The empirical results show that our approach is more efficient while achieving almost same performance.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiang Cao

Authors
  1. Xiang Cao
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Correspondence to Xiang Cao .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Cao, X. (2015). A Scalable and Feasible Matrix Completion Approach Using Random Projection. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_62

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  • DOI: https://doi.org/10.1007/978-3-319-26555-1_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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